Old-School Knowledge Management is Eating Away at your SMEs’ Time, Productivity

Imagine, for a moment, that you are involved with an important update. Maybe you were an engineer or coder on the project, or perhaps you were a product manager charged with seeing the update through. Consider:

  • How do you announce the update to the rest of your organization?
  • How do you field the dozens (maybe even hundreds) of potential questions, once that announcement is made?
  • Most importantly, how do you make that information “top of mind” when the sales team is out in the field?

In a previous post, we discussed the disconnect that often occurs between sales teams, on the one hand, and subject matter experts (SMEs), on the other. In that article, our focus was on how this disconnect affects sales productivity. Sales reps often rely on sales enablement and product/technical marketing to provide them with technical information about product features, capabilities, solutions, and roadmaps; searching for that information can eat away at reps’ valuable time.

It is also worth exploring how various SMEs—including product experts, engineers, project managers, and even marketing specialists—are affected by this gap. As it turns out, SMEs are often hindered by outdated methods of knowledge management. It’s time for modern enterprises to fix that.

Every Subject Matter Expert Deals with These Hassles

Speaking with SMEs from a number of industries, one finds some common threads in the kinds of problems they identify as hindrances to their productivity. Here are the top four we discovered:

#1: Repetitive Requests for Information

Based on conversations with SMEs, we estimate that more than a third of the requests for information that SMEs receive on a daily basis are requests for redundant information—that is, information the SME has already made available. This could be because:

  • Multiple sales reps all ask the same question separately,
  • The information is readily available in an electronic document or wiki, but the rep did not have access or know about the resource,
  • A question was answered in a previous conversation, but there was no easy way to uncover the answer, or
  • Any combination of these.

Huge amounts of SME time are occupied with responding to questions that have already been asked and answered, and providing documentation that is readily available. Knowledge management tools might help organize and centralize this information, but distribution is still a problem. Sales and customer service reps need this information at their fingertips, in real time—a luxury that produces an undue burden on SMEs.

#2: Documentation Woes

SMEs are often called upon to create content that is, in turn, consumed by many others in the organization. In many large and enterprise-sized organizations, those SMEs will use wikis and other content management systems to make information available. Uptake and use of these systems varies by company and is largely affected by the company culture, amount of training on the system, and comfort with the technology.

Indeed, many sales and service reps are unaccustomed to using the tools that are the bread-and-butter for SMEs. This means that they often cannot (or will not) get the most up-to-date information.

This has a subtle but large impact on the effectiveness of sales messaging. Indeed, 55% of organizations claim that they fail to communicate their value effectively because reps fail to find and utilize tailored content. In essence, all the documentation that SMEs are creating becomes wasted effort if not used in a timely manner.

#3: Lack of Quality Checks on Content

Even when SMEs create content and make it available, sales teams will still create their own. For example, an engineer might develop dozens of technical documents and sales sheets, but the sales team will still create their own PowerPoint deck.

The problem with traditional knowledge management systems is that there is no good mechanism to check the accuracy and quality of that non-SME-generated content. Information tends to flow from the experts to the frontline employees, but there is no system in place to flag content for expert review and facilitate feedback.

This creates a huge amount of frustration for SMEs. It also causes problems for sales teams, who must often “walk back” promises made or qualify information they have given customers, all of which negatively affects the brand.

#4: Frequent Training Might Not Be an Option

One common solution to the above for many organizations is training. Companies will attempt to bring their frontline employees, as well as their SMEs, up-to-date on the latest product and market information.

This is becoming less and less of an option. Larger organizations could well be dealing with thousands of products, all of which have frequent updates and expert-generated content. Sitting everyone down in a room for an afternoon soon becomes unscalable. Providing training for older content management systems only exacerbates the problem.

#5: Much Information is “Long Tail”

The information needed to address a customer’s question or complaint can often be highly specific, and not general at all. In content circles, this is known as “long tail” information. With traditional knowledge management, that information might be buried deep down in some faq or spec sheet. The more bits of such “long tail” information there are, the worse the problem becomes. SMEs often end up spending huge amounts of time trying to organize this long tail information, and yet those across the organization who need it still cannot find the specific bits they need.

Getting SMEs Productive Again

We’ve found that the above complaints have a common, core cause. SMEs are generating large amounts of information, both formally and informally, that are highly “consumed” across the organization. But, as much of this information includes smaller pieces of highly specific (long tail) knowledge, it is virtually impossible to structure and manage this information without creating huge burdens for someone.

This means that one must be willing to abandon the older model of disseminating information within an enterprise organization. Modern technology can then be used to automatically build the needed structures on the fly.

For example:

  • Product updates can be handled in real time without the need for a centrally managed database.
  • APIs allow for seamless integration of existing systems such as CRM, sales enablement, customer service, and more.
  • Data-driven (vs. algorithm-driven) search tools allow for real-time search of unstructured data, including not only wikis and databases, but email conversations, chat channels, and more.
  • Better user interfaces put relevant answers to questions right at an employee’s fingertips, at the moment they are needed.

Solutions that bundle these technologies, like our own Nimeyo, often go under the label of knowledge automation. Knowledge automation makes the dissemination of information faster and more seamless, allowing SMEs to focus on their primary tasks and without the need for extensive training. Nimeyo, for example, automatically builds structures from SMEs contributions and dynamically disperses them to the right individuals, addressing the above complaints in an elegant and scalable manner.

And in the end, is this not the goal? To keep information accessible, useful, and relevant without overburdening those tasked with creating it in the first place?

Two AI Use Cases for Customer Support and Services

In our last post, we highlighted the fact that many companies assume that the more “human” parts of business —sales and customer service—have little to gain from Artificial Intelligence. Of course, this assumption is incorrect, and liable to mislead companies who could otherwise stand to benefit.

Consider:

  • According to Forrester, 72% of businesses say that improving the customer experience is their top priority.
  • Most contact with customer service now takes place via the web using a chatbot, via email, or via social media. The set of skills and tools needed here are different than, say, handling a case via phone.
  • Customers have ever-growing expectations with regards to response time. A decade ago, customers were willing to wait 24 hours for an answer to a question or a solution to their problems. Now they want an answer right away…if not instantly.
  • As business grow and expand their global reach, more and more customer support cases begin to look similar. Solving each case independently is burdensome, if not impossible.
  • The average customer triage and resolution cycle takes five or more steps having to do solely with information search among the organizations various data sources.

In other words, the need for a human being with “people skills” is diminishing just as the strengths of artificial intelligence agents—such as the ability to query multiple data sources quickly and efficiently—are coming in high demand. Indeed, one prediction holds that, by the year 2020, more than 85% of all customer interactions will be handled without the need for a human agent.

But what does customer support via artificial intelligence agent look like? Again, we can illustrate this best with two use cases around our own knowledge automation solution, Nimeyo.

Use Case 1: Resolving Customer Issues When Knowledge is Siloed

The Context:

Today, customer service reps are expected to resolve customer issues faster and faster, even as they take on huge case volume to justify their job roles. In order to do a good job of meeting customer expectations and succeed in their roles, a single pane of information and knowledge access is essential.

The Challenge:

Again, the typical customer resolution in an organization of any appreciable sizes takes researching five or more data-sources. These include:

  • Querying customer-facing case management systems (such as Salesforce Service Cloud or Zendesk) to identify duplicates and bring up relevant contextual information
  • Comparing across internal incident management systems (such as Jira) to find similar cases already being addressed, or that have recently been addressed successfully.
  • Searching KnowledgeBase articles and wikis for quick resolution of common problems, or concise answers to frequently asked questions
  • Combing through off-band but relevant conversations in emails or Slack channels

Already, this process is pretty daunting. When you consider that two or more of these steps could be taken for cases that are very similar, and for which solutions already exist, it becomes painfully obvious how much time is wasted and productivity sacrificed. Currently, organizations are struggling to find ways to integrate these various sources into a single pane.

How AI Helps:

This scenario is easily fixed with a solution like Nimeyo knowledge automation. Using Nimeyo, customer service reps can address cases more readily, thanks to instant access to knowledge of similar cases across content silos of customer issues and internal product ticket systems.

Nimeyo can also integrate with management systems like Salesforce, as well as incident management systems like Jira and chat channels like Slack. It can then access these systems instantly and use the information in them to help zero-in on the resolution for a given case, relieving the customer service rep from having to do these searches manually.

More importantly, Nimeyo helps customer service reps deflect more cases by giving them increased visibility of similar cases across customer issues and internal product ticket systems.

All of this results in more rapid response which, ideally, leads to improving their first contact and/or first time resolution times.

Use Case 2: Self-help Bots For Customers and Service Teams

The Context:

As we all know, a lot hinges on having a positive customer service experience: It can mean the difference between a loyal customer, and a disgruntled one. Speed and accuracy matter crucially, and customer demand instant responses. If they don’t find an answer immediately, they are disappointed and are quick to share their bad experience on social media or other public channels.

But increasing complexity of products and services, along with the high turnover rate of most call centers, means that it is almost impossible for service reps to keep up with the content needed to resolve issues in a timely fashion.

These dynamics are fundamentally changing how both customers and service reps seek out information. For example, the majority of Millennials actively avoid situations for which human interaction is necessary to solve an issue, much preferring self-service options instead. One study of the generational divide in customer service found that a whopping 72% of Millennials believe a phone call is not the best way to resolve their customer service issues.

So how are consumer resolving their issues, if not calling customer support? Right now, they are using a mix of chat bots on websites, social media sites for the relevant brands, chat channels, and Google searches. In other words, they are already going with digital self-help solutions.

The Challenge:

Companies face two choices: Either improve the self-help bots they make available, or better empower their service teams to compete with these bots.

Most of the current self-help systems are web centric, so customers are relegated to searching for a solution themselves—and are often confronted with more pages than they are willing to review. Even if they do find the  answer they seek, it may not be the most accurate or latest answer.

That said, many Customers are still “put at ease” knowing that there is a customer service rep in the interaction; but this “human touch” engagement is costly, often only available during business hours, and is (for the most part) unscalable.

How AI Helps:

With a Nimeyo AI solution, customer service organizations can create a foundation of knowledge and insights from approved content sources like FAQ databases, product documentation or issue tracking systems. Subsequently, bots or auto responses act as the first line of defense to respond to common question with known answers or fixes.

When a customer sends an email to a support email address, the email autoresponder can look at the knowledge available to instantly respond with links to most relevant answer. If the customer is happy with the answer, then the customer service team can mark the issue as resolved. If the customer indicates that more assistance is needed, a rep can reach out for additional information.

What about availability and scale? Typically, a customer service chat is available only during business hours (unless you have a globally distributed service teams.) However, if a customer initiates a chat with a rep during off hours, an auto responder bot can respond to customer query with knowledge from approved sources. Queries such as the status of a case, answers to FAQs, or product specific questions can be responded to in seconds without any human intervention. Again, the chatbot can be the first line of defense before a rep needs to be engaged.

Again, these are just two simple ways that AI agents are changing the face of customer service. Counter to many of the assumptions surrounding AI, human beings will always have a role to play in customer support, since there will always be difficult cases requires a person’s  ability to understand the nuances of the case and find creative solutions. Increased productivity comes when human beings can be freed from routine and easily-solved cases, and allowed to focus on more complex cases and tasks. Artificial Intelligence can potentially leave service reps free to tap into the critical thinking and problem-solving skills, not to mention emotional intelligence, when they are needed most.

If it still sounds like a pipe-dream to empower human interactions through AI technology, we recommend you try Nimeyo yourself to see how this can be done in your organization. Sign up for a free demo, and we would be delighted to give you a tour and show how the Nimeyo AI can be best used by your customer service teams.